Dual-Branch Chinese Spelling Correction Method for Target Personal Names

Chinese personal name correction is an important problem in Chinese spelling correction (CSC) and has been widely applied in search correction, writing assistance, and post-processing of recognition results. Existing name correction methods often rely too heavily on name boundary detection, candidate name sets, or external knowledge resources, making them susceptible to biases caused by errors in obtaining candidate names and insufficient resource coverage. To address these problems, we incorporate Chinese personal name correction into a unified CSC framework and propose a Dual-Branch Chinese Spelling Correction (DCSC) method for the character-level correction of target personal names. Firstly, an online correction dataset for Chinese target person names is constructed. This dataset was created by replacing the personal pronouns in the existing Chinese corpus and generating a series of erroneous name samples, thereby avoiding the problem of incorrect candidate names due to errors in name boundary detection. Furthermore, we present a dual-branch gated fusion module. This module consists of a name-specific branch and a general branch, which respectively model the name-specific features and the general correction features. Through this module, Chinese personal name correction and general CSC are integrated into a single framework. In addition, a gating mechanism is employed to automatically fuse the features obtained by the two branches, thereby reducing the interference of the training for specific names on the overall correction ability of the model. DCSC not only can accurately correct errors in personal names, but also does not significantly reduce the overall spelling correction performance of the model. On the three classical CSC models, namely, MDCSpell, SCOPE, and NamBert, our method achieved F1 scores of 94.14%, 95.10%, and 93.95%, respectively, on the proposed online Chinese personal name correction dataset. This indicates that our method can effectively correct both Chinese personal names and common spelling errors simultaneously.